no code implementations • 9 Sep 2023 • Biplav Srivastava, Kausik Lakkaraju, Tarmo Koppel, Vignesh Narayanan, Ashish Kundu, Sachindra Joshi
Chatbots, the common moniker for collaborative assistants, are Artificial Intelligence (AI) software that enables people to naturally interact with them to get tasks done.
no code implementations • 25 Jul 2023 • Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Raghava Mutharaju, Michael N. Huhns, Vignesh Narayanan
Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse.
no code implementations • 24 Jun 2023 • Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth
Crowdsourced and expert-curated knowledge graphs such as ConceptNet are designed to capture the meaning of words from a compact set of well-defined contexts.
no code implementations • 23 Jun 2023 • Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth
However, the ad-hoc nature of existing methods makes it difficult to properly analyze the effects of knowledge infusion on the many moving parts or components of a transformer.
no code implementations • 16 Jun 2023 • Kaushik Roy, Yuxin Zi, Manas Gaur, Jinendra Malekar, Qi Zhang, Vignesh Narayanan, Amit Sheth
In this study, we introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm that layers clinical process knowledge structures on language model outputs, enabling clinician-friendly explanations of the underlying language model predictions.
Explainable Artificial Intelligence (XAI) Language Modelling
1 code implementation • 1 Jun 2023 • Revathy Venkataramanan, Kaushik Roy, Kanak Raj, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit Sheth
In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and ingredients, to also incorporate cooking actions (e. g., add salt, fry the meat, boil the vegetables, etc.).
no code implementations • 8 May 2023 • Kaushik Roy, Tarun Garg, Vedant Palit, Yuxin Zi, Vignesh Narayanan, Amit Sheth
However, they do not ascribe object and concept-level meaning and semantics to the learned stochastic patterns such as those described in knowledge graphs.
no code implementations • 16 Dec 2022 • Bharath Muppasani, Vishal Pallagani, Kausik Lakkaraju, Shuge Lei, Biplav Srivastava, Brett Robertson, Andrea Hickerson, Vignesh Narayanan
Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks.
no code implementations • 9 Oct 2022 • Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth
Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.
1 code implementation • NAACL (CLPsych) 2022 • Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
no code implementations • 13 Dec 2021 • Wei Miao, Vignesh Narayanan, Jr-Shin Li
The reservoir computing networks (RCNs) have been successfully employed as a tool in learning and complex decision-making tasks.
no code implementations • 28 Oct 2021 • Krishnan Raghavan, Vignesh Narayanan, Jagannathan Sarangapani
In this paper, we address two key challenges in deep reinforcement learning setting, sample inefficiency and slow learning, with a dual NN-driven learning approach.
no code implementations • 28 Oct 2021 • Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani
Learning to control complex systems using non-traditional feedback, e. g., in the form of snapshot images, is an important task encountered in diverse domains such as robotics, neuroscience, and biology (cellular systems).
no code implementations • 9 Dec 2014 • Vignesh Narayanan, Yu Zhang, Nathaniel Mendoza, Subbarao Kambhampati
While information asymmetry can be desirable sometimes, it may also lead to the robot choosing improper actions that negatively influence the teaming performance.